Job Description
Join QuantumLeap Dynamics at the forefront of technological evolution as we pioneer breakthrough AI solutions for 2026. We seek visionary researchers to transform theoretical concepts into scalable, ethical, and revolutionary applications that will redefine industries. Our state-of-the-art lab in San Francisco offers unparalleled resources to explore quantum computing, neural networks, and autonomous systems.
Collaborate with Nobel laureates and industry disruptors in an environment that values intellectual curiosity and bold innovation. We provide competitive equity packages, flexible work arrangements, and dedicated R&D funding for your most ambitious projects. Shape the future of human-machine interaction while maintaining our unwavering commitment to ethical AI development and responsible innovation.
Responsibilities
- Lead advanced research in generative AI, quantum machine learning, and human-AI collaboration frameworks
- Develop and prototype next-gen AI architectures for autonomous systems and predictive analytics
- Publish high-impact research in top-tier journals and present at global tech conferences
- Mentor junior researchers and cross-functional teams in agile development methodologies
- Ensure compliance with evolving AI ethics regulations and safety protocols
- Collaborate with product teams to translate research into market-ready solutions
- Secure external funding through government grants and industry partnerships
Qualifications
- PhD in Computer Science, AI, or related field with 5+ years industry research experience
- Expertise in transformer architectures, reinforcement learning, and multi-agent systems
- Proven track record of publishing in NeurIPS/ICML/ICLR or equivalent venues
- Strong programming proficiency in Python, PyTorch, and distributed computing frameworks
- Deep understanding of quantum computing principles and quantum algorithms
- Demonstrated ability to lead complex, multi-year research initiatives from concept to deployment
- Commitment to ethical AI development and transparent research practices